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from lightning import LightningDataModule
from torch.utils.data import Dataset, DataLoader
class Datamodule(LightningDataModule):
def __init__(
self,
train_dataset: Dataset,
eval_dataset: Dataset,
batch_train_size: int,
num_workers: int,
eval_batch_size: int = None,
):
super().__init__()
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.batch_train_size = batch_train_size
self.eval_batch_size = (
eval_batch_size if eval_batch_size is not None else batch_train_size
)
self.num_workers = num_workers
def train_dataloader(self) -> DataLoader:
"""Load train set loader."""
persistent_workers = True if self.num_workers > 0 else False
dataloader = DataLoader(
self.train_dataset,
batch_size=self.batch_train_size,
num_workers=self.num_workers,
pin_memory=True,
persistent_workers=persistent_workers,
)
return dataloader
def val_dataloader(self) -> DataLoader:
"""Load val set loader."""
persistent_workers = True if self.num_workers > 0 else False
dataloader = DataLoader(
self.eval_dataset,
batch_size=self.eval_batch_size,
num_workers=self.num_workers,
pin_memory=True,
persistent_workers=persistent_workers,
)
return dataloader
def predict_dataloader(self) -> DataLoader:
"""Load predict set loader."""
dataloader = DataLoader(
self.eval_dataset,
batch_size=self.eval_batch_size,
num_workers=self.num_workers,
)
return dataloader
def test_dataloader(self) -> DataLoader:
"""Load test set loader."""
dataloader = DataLoader(
self.eval_dataset,
batch_size=self.eval_batch_size,
num_workers=self.num_workers,
)
return dataloader
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